---
title: "F.S.Bellelli --- City of London"
output:
flexdashboard::flex_dashboard:
storyboard: true
source: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dygraphs)
library(leaflet)
library(tidyverse)
library(plotly)
library(htmltools)
months <- data.frame(name=c("January","February","March","April","May","June","July","August","September","October","November","December"), number = sprintf("%02d",seq(1,12)))
```
### This case study will focus on the City of London
### **1)** The City of London is an iconic location for office space.
```{r}
leaflet() %>%
addTiles() %>%
# addProviderTiles(providers$Stamen.TonerLite) %>%
# addProviderTiles(providers$Stamen.Watercolor) %>%
# addProviderTiles(providers$CartoDB.Positron) %>%
# addProviderTiles(providers$Stadia.AlidadeSmooth) %>%
# addProviderTiles(providers$Thunderforest.Pioneer) %>%
# addProviderTiles(providers$Thunderforest.Neighbourhood) %>%
addRectangles(
lng1=-0.079, lat1=51.520,
lng2=-0.105, lat2=51.5085,
fill=TRUE,
fillColor = "navy",
fillOpacity = 0.08
)%>%
addMarkers(lng=-0.09203944557623281, lat=51.513219744574855, popup="City of London")
```
***
The City of London is an iconic location for office space
- Located in central London, the City is the primary business district of London.
- The city has special administrative status. It is considered a separate "city" within London.
- As a result, the City enjoys regulatory independence.
- The City has a surface of only $2.9km^2$. Hence, new real-estate developments often take the form of high-rise buildings.
- In 2016, the City only had 9,400 residents, but over 500,000 persons regularly commuting for work.
### **2)** The City is the beating heart of London's financial, legal and international business activity
***
- The City of London is a global financial district. However, only 9% of its office space is directly occupied by the financial and insurance industry.
- The legal industry occupies almost a third of all office space. Many international law firms have their headquarter in the "Magic Circle" in the City of London.
- Media and Tech businesses occupy 34% of office space. For instance, Amazon UK has its headquarters here. Several publishing and newspaper companies also have their offices in the City (famously on *Fleet street*). This category also includes a large number fintech companies.
- In the past, business and retail services had a strong presence in the City, but they have now moved to the West End.
### **3)** Like the rest of Europe, the City's office market has been heavily affected by the pandemic
```{r, fig.align='center'}
data <- read.csv("clean data/SAVILLS city office market watch data.csv")
colnames(data)[1]<-"Date"
data$month <- str_sub(data$Date,1,-6)
data$year <- str_sub(data$Date,-4,-1)
data <- left_join(data,months,by=c("month"="name"))
data$date <- as.Date(paste0(data$year,"-",data$number,"-01"))
data$takeup <- as.numeric(str_replace_all(data$takeup,",",""))
data$X12m_takeup <- as.numeric(str_replace_all(data$X12m_takeup,",",""))
data$gradeA_rent <- as.numeric(str_replace_all(data$gradeA_rent,"£",""))
data$vacancy_rate <- as.numeric(str_replace_all(data$vacancyrate,"%",""))
data$takeup_12m <- (data$X12m_takeup/ 10.764)/1000 #convert to sq metres
ggplot(data, aes(x = date, y = vacancy_rate)) +
annotate(geom = "rect", xmin = as.Date("2020-02-01"), xmax = as.Date("2021-08-01"), ymin = 0, ymax = 10, fill = "navy", alpha = 0.05)+
geom_line(colour="navy", size=1.3)+
geom_line(aes(x=date,y=(takeup_12m-200)/73.5), colour="firebrick", size=1.3)+
scale_y_continuous("Vacancy rate (%)",limits= c(0,10), expand= c(0,0), sec.axis = sec_axis(~.*73.5 + 200 , name = "12 months take-up (thousand of sq metres) ")) +
scale_x_date("Year",expand=c(0,0))+
theme_bw()+
annotate(geom="text", x=as.Date("2020-11-01"), y=9.5, label="COVID-19", color="black", size=4) +
annotate(geom="text", x=as.Date("2019-01-01"), y=8, label="Office space take-up\nCity of London (rhs)", color="firebrick", size=3.5) +
annotate(geom="text", x=as.Date("2019-01-01"), y=4.3, label="Office vacancy rate\nCity of London (lhs)", color="navy", size=3.5) +
labs(caption="Data source: Savills")
#ggplotly(p) %>%
# layout(yaxis2 = list(overlaying = "y",
# side = "right",
# title = "12 months take-up (thousand of sq metres)"))
```
***
The pandemic has shaken London City's office market.
- In 2021 vacancy rate was historically high. Almost 10% of all office property is not currently occupied.
- As the pandemic unfolded, many companies decided to cut their office space.
- This demand shock weighted negatively on office rental prices.
### **4)** Conversely, remote working increased demand for residential space in London and pushed home prices upward.
```{r, fig.cap="Data source: UK Land Registry"}
data <- read.csv(file = "clean data/house price index london.csv")
data$month <- str_sub(data$Date,1,-6)
data$year <- str_sub(data$Date,-4,-1)
data <- left_join(data,months,by=c("month"="name"))
data$date <- as.Date(paste0(data$year,"-",data$number,"-01"))
# data <- as.data.frame(data)[,c("date","house_price_index_london")]
# data <- xts::xts(data[,2], order.by = data[,1])
# dygraph(data) %>%
# dySeries("V1", label="London house price index", color="navy", strokeWidth = 3) %>%
# dyRangeSelector() %>%
# dyAxis("y", label = "London house price index") %>%
# dyShading(from = "2020-2-1", to = "2021-9-1") %>%
# dyEvent("2020-2-1", "COVID-19 in the UK", labelLoc = "bottom")
ggplot(data, aes(x = date, y = house_price_index_london)) +
annotate(geom = "rect", xmin = as.Date("2020-02-01"), xmax = as.Date("2021-08-01"), ymin = 110, ymax = 130, fill = "navy", alpha = 0.05)+
geom_line(colour="navy", size=1)+
scale_y_continuous("London house price index",limits= c(110,130), expand= c(0,0)) +
scale_x_date("Year",expand=c(0,0))+
theme_bw()+
annotate(geom="text", x=as.Date("2020-11-01"), y=128, label="COVID-19", color="black", size=4)
# ggplotly(p)
```
***
- A large proportion of City's workers are still working from home.
- Remote working increased demand for residential space in London and pushed home prices upward.
### **5)** The worst of the pandemic seems to have passed and vaccination rate is high in London
```{r}
deaths <- read.csv("clean data/covid_ons_deaths_cqc_by_la.csv")
cases <- read.csv("clean data/covid_phe_cases_london_boroughs.csv")
cases <- cases %>% group_by(date) %>% summarise(new_cases = sum(new_cases),
total_cases = sum(total_cases),
.groups = "drop")
deaths <- deaths %>% filter(cause_of_death == "COVID 19") %>% group_by(date) %>%
summarise(deaths = sum(deaths), .groups="drop")
corona <- full_join(cases,deaths, by="date")
corona <- corona %>%
mutate(new_cases = zoo::rollapplyr(new_cases, 7, mean, na.rm = TRUE, fill=NA),
deaths = zoo::rollapplyr(deaths, 7, mean, na.rm = TRUE, fill=NA))
corona$date <- as.Date(corona$date)
corona <- as.data.frame(corona)
corona$total_cases <- NULL
corona <- xts::xts(corona[,2:3],order.by = corona[,1])
dygraph(corona, group="covid")%>%
dySeries("new_cases", label="New cases", strokeWidth = 2) %>%
dySeries("deaths", axis = 'y2', label="Deaths", strokeWidth = 3) %>%
dyOptions(colors = c("navy","firebrick"), fillGraph = TRUE, fillAlpha = 0.3, stepPlot = TRUE) %>%
dyRangeSelector(dateWindow = c("2021-01-01", "2021-08-01")) %>%
dyAxis("y", label = "New cases") %>%
dyAxis("y2", label = "Deaths")
# vaccination <- read.csv("clean data/covid_phe_vaccines_age_london_boroughs.csv")
# vaccination <- vaccination %>% group_by(date, dose) %>%
# summarise(cum_doses = sum(cum_doses),
# cum_prop = sum(cum_doses)/sum(population),.groups ="drop")
# vaccination$dose <- str_replace_all(vaccination$dose, "1st ","first_")
# vaccination$dose <- str_replace_all(vaccination$dose, "2nd ","second_")
# vaccination$cum_prop <- 100*vaccination$cum_prop
# prova <- pivot_wider(vaccination[,c(1:2,4)],id_cols="date",values_from ="cum_prop",names_from="dose") %>% as.data.frame()
# prova$date <- as.Date(prova$date)
# prova <- xts::xts(prova[,2:3],order.by = prova[,1])
#
# dygraph(prova,main="... thanks to UK's high vaccination rate", group="covid")%>%
# dySeries("second_dose", label="Second dose", strokeWidth = 2, color="navy", fillGraph = TRUE) %>%
# dySeries("first_dose", label="First dose", strokeWidth = 2, color="cornflowerblue", fillGraph = TRUE) %>%
# dyOptions(stepPlot = FALSE, fillAlpha = 0.7) %>%
# dyRangeSelector(dateWindow = c("2021-01-01", "2021-08-01")) %>%
# dyAxis("y", label = "Vaccination rate (%)", valueRange = c(0, 101))
# ggplot(vaccination)+
# geom_ribbon(aes(x=as.Date(date), ymin=0, ymax=cum_prop*100, alpha=dose),fill="navy", size=1.3, show.legend = FALSE)+
# theme_bw()+
# labs(x="Date",y="Vaccination rate of London residents (%)", caption = "Data source: Public Health England")+
# annotate(geom="text", x=as.Date("2021-07-10"), y=25, label="Second dose", color="white", size=4) +
# annotate(geom="text", x=as.Date("2021-07-10"), y=65, label="First dose", color="navy", size=4) +
# scale_x_date(date_labels = "%b/%Y", limits=as.Date(c("2020-12-08","2021-08-10")),
# breaks= seq.Date(as.Date("2021-01-01"),as.Date("2021-08-01"), by="month"), expand=c(0,0))+
# scale_y_continuous(limits = c(0,100),expand=c(0,0))+
# theme(panel.grid.minor.x = element_blank())
#
```
***
- The UK was hit hard by the pandemic, but the worst of the seems to have passed
- London has a high vaccination rate. More than 80% of the population has received at least the first dose of vaccination and more than 60% already received their second dose.
- The hight vaccination rate protected the population during the latest surge of the Delta variant. Unlike previous surges, the number of hospitalisation and deaths rose only moderately following the boom in cases.
### **6)** As a result, people are gradually returning to the City's offices
```{r,fig.width=10, fig.asp=0.5, fig.align ="center"}
library(data.table)
library(zoo)
data <- fread("clean data/mobility_report_europe.csv")
data <- data[`sub region 2`=="City of London", c("date","workplaces") ]
data <- data %>% mutate(work = c(NA,NA,zoo::na.approx(workplaces)),
smooth = zoo::rollapplyr(work, 7, sum, na.rm = TRUE, fill=NA)/7)
ggplot(data, aes(x=as.Date(date), y=workplaces))+
geom_hline(yintercept = 0)+
geom_line(colour="navy",alpha=0.3)+
geom_line(aes(x=as.Date(date), y=smooth), colour="navy", size=1.3)+
theme_bw()+
labs(x="Date",y="Google mobility index (% compared to baseline)", caption = "Data source: Google")+
annotate(geom="text", x=as.Date("2021-06-10"), y=-43, label="7-days travel to\n City's workplaces", color="navy", size=4) +
scale_x_date(date_labels = "%b/%Y", limits = as.Date(c("2020-02-17","2021-09-01")), expand=c(0,0))
```
***
- COVID restriction in the UK have been almost completely lifted.
- However, mobility data shows a slow return to offices for the moment.
- Physical presence in the City will probably increase more sharply when the latest COVID surge ebbs away.
- To some extent, teleworking and alternative working arrangement will probably remain in place even after the end of the pandemic. This could reduce the demand for large office spaces.
### **7)** Despite Brexit and COVID-19, the City has solid growth prospects
```{r, fig.align="center"}
population <- read.csv("clean data/population projection london.csv")
population <- population[population$borough == "London (total)", c("year","population","international.net","domestic.net","total.change")]
population$population <- population$population/1000000
gdp <- read.csv("clean data/GDP forecast IMF July 2021 WO.csv")
data <- full_join(gdp,population, by="year") %>% as.data.frame()
# data <- xts::xts(data[,2:6],order.by = as.Date(as.character(data[,1]), format="%Y"))
# dygraph(data[,2:3])%>%
# dySeries("GDP_n", label="GDP UK", strokeWidth = 2) %>%
# dySeries("population", axis = 'y2', label="London population", strokeWidth = 2) %>%
# dyOptions(colors = c("navy","firebrick"), fillGraph = FALSE, stepPlot = FALSE) %>%
# dyRangeSelector(dateWindow = c("2009-01-01", "2031-01-01")) %>%
# dyAxis("y", label = "GDP (billion USD)") %>%
# dyAxis("y2", label = "Population of London (millions)") %>%
# dyShading(from = "2021-1-1", to = "2050-1-1") %>%
# dyEvent("2021-1-1", "Forecast", labelLoc = "bottom")
ggplot(data)+
annotate(geom = "rect", xmin = 2020.5, xmax = 2050, ymin = 0, ymax = 4500, fill = "navy", alpha = 0.05)+
geom_line(data=data[data$year<2021,],mapping=aes(x=year,y=(population-8)*1600), colour="firebrick", size=1.3)+
geom_line(data=data[data$year>=2021,],mapping=aes(x=year,y=(population-8)*1600), colour="firebrick", size=1.3, linetype="dashed")+
geom_line(data=data[data$year<2021,],mapping=aes(x=year,y=GDP_n), colour = "navy", size=1.3)+
geom_line(data=data[data$year>=2021,],mapping=aes(x=year,y=GDP_n), colour = "navy", size=1.3, linetype= "dashed")+
scale_y_continuous("GDP (in billion USD)",limits= c(0,4500), expand= c(0,0), sec.axis = sec_axis(~./1600 + 8 , name = "Population of London (in millions)"))+
scale_x_continuous("Year",limits= c(1980,2050), expand= c(0,0))+
theme_bw()+
labs(caption="Data source: Greater London Authority / IMF July 2021 GDP forecasts")+
annotate(geom="text", x=2036, y=300, label="FORECAST", color="navy", size=4)+
annotate(geom="text", x=2004, y=900, label="Population of London\n(rhs)", color="firebrick", size=3.5) +
annotate(geom="text", x=1988, y=1500, label="GDP (lhs)", color="navy", size=3.5)
```
***
- Despite Brexit and COVID-19, the City has solid growth prospects and the City is unlikely to lose its position as financial and business hub.
- London remains an attractive place for international corporations and finance. It offers easy access to skilled workforce, a supportive government, good infrastructures, easy access to customer base, and the opportunity to enter in the local network of businesses and tech clusters.
- London population is expected to reach 10 million by 2040. Much of this population will come from international immigration.
- In its latest forecast, the IMF predicts the British economy will grow at 5% rate in 2021 and 2022 and almost 2% until 2025.
- Given the uniqueness of the location and the limited supply, prospects for the office market in the City are positive in the long run.
### **8)** A simple forecast model
A simple model
$$ y=\beta \cdot x+\epsilon$$
#### Title 4
Yet another simple model $y=x$
